LIDAR sensors are valuable tools for obtaining information about surroundings in an environment such as distances to various objects, identities of objects, and so on. Consequently, LIDAR sensors are becoming more common in vehicles and especially in autonomous vehicles. However, while LIDAR sensors can be highly accurate and useful for identifying objects, these sensors are also subject to errors. For example, when a LIDAR sensor is first manufactured a calibration process is generally undertaken to adjust the operation of the sensor and ensure proper alignment. As the LIDAR sensor is used, various environmental conditions (e.g., vibrations, temperature changes, etc.) can cause the LIDAR sensor to drift out of alignment.
However, calibrating the LIDAR sensor can be a complex task. For example, various methods for calibrating generally include using a pre-established environment that includes static markers with known and tested attributes (e.g., distances and shapes). Thus, this established environment needs to be separately maintained so that the LIDAR can be calibrated. Consequently, calibrating the LIDAR sensor can introduce costs from maintaining the static location. Accordingly, a likelihood that the sensors will be calibrated on a regular basis is lessened because of difficulties with accessing such static locations.